Random

Overview

The Random Node.

The Random Node generates a random outcome, usually a number.

This Node can be set to three different Modes (Advanced, Expert, and Standard). Each of these Modes offers a different set of Attributes that are explained below.

Scope: Project, Scene, Function, Prefab.

Attributes

The Random Node Attributes.

Each Mode has a different set of Attributes. The Modes are: Advanced, Expert, and Standard.

Advanced

Generator

This Mode allows the user to choose whether the random generator is deterministic or not, and for the deterministic case, the seed to use.

Attribute	Type	Description
Is Deterministic	Bool	Whether the random generator is deterministic or not.
Seed	Int (only available when Is Deterministic is set to true)	The Seed to use for the deterministic random generator.

Distribution

This Mode has a Drop-down menu from which the probability distribution used for the random generator can be chosen. Each option offers its own set of Attributes with the probability distribution parameters.

Attribute	Type	Description
Distribution	Drop-down	The probability distribution that the random generator will use.

Next, the Attributes for each probability distribution are described. For each probability distribution, the link to its corresponding Wikipedia entry is given.

• Bernoulli

Probability distribution of a random variable that can take two values: true, with probability p; and false, with probability 1-p. When this distribution is chosen, the outcome of the Node is a Boolean.

Attribute	Type	Description
Probability of 'true'	Float (between 0 and 1)	The probability that the outcome will be true.

• Binomial

Probability distribution of the number of successes in a sequence of independent experiments, each one with two possible outcomes: success and failure. The parameters for this probability distribution are the number of experiments and the probability of a successful outcome in each one.

Attribute	Type	Description
Data Type	Drop-down	Whether the outcome will be an Int or Byte.
Probability of 'true'	Float	The probability that the outcome of each trial is true.
Number of trials	Int	The number of independent experiments, each one with probability of success Probability of 'true'.

• Normal

Symmetric probability distribution, with half its values less than the mean and half greater than the mean. The parameters are the mean, which equals the median and the mode, and the standard deviation.

Attribute	Type	Description
Mean	Float	The mean value of the distribution.
Standard deviation	Float	The standard deviation of the distribution.

• Poisson

Discrete probability distribution that expresses the probability of a given number of events occurring in a specified time period. Its parameter is the mean value.

Attribute	Type	Description
Data Type	Drop-down	Wheter the outcome will be an Int or Byte.
Mean	Float	The mean value of the distribution.

• Uniform

Probability distribution in which all the values in an interval are equally likely to be drawn. It can either be continuous or discrete.

Attribute	Type	Description
Data Type	Drop-down	Whether an Int, Float, or Byte will be generated.
Minimum	Defined in the Data Type Attribute	The lower bound of the interval from which the random number will be extracted.
Maximum	Defined in the Data Type Attribute	The upper bound of the interval from which the random number will be extracted.

Expert

Generator

This Mode allows to choose from a list of several types of random generators.

Attribute	Type	Description
Generator	Drop-down	The type of random generator to use.
Seed	Int (not available for non_deterministic Generator)	The Seed to use for the random generator.

Distribution

This Mode has a Drop-down menu from which the probability distribution to be used for the random generator can be chosen. Each option offers its own set of Attributes with the probability distribution parameters.

Attribute	Type	Description
Distribution	Drop-down	The probability distribution that the random generator will use.

Next, the Attributes for each probability distribution are described. For each probability distribution, the link to its corresponding Wikipedia entry is given.

• Bernoulli

Probability distribution of a random variable that can take two values: true, with probability p; and false, with probability 1-p. When this distribution is chosen, the outcome of the Node is a Boolean.

Attribute	Type	Description
Probability of 'true'	Float (between 0 and 1)	The probability that the outcome will be true.

• Binomial

Probability distribution of the number of successes in a sequence of independent experiment, each one with two possible outcomes: success and failure. The parameters for this probability distribution are the number of experiments and the probability of a successful outcome in each one.

Attribute	Type	Description
Data Type	Drop-down	Whether the outcome will be an Int or Byte.
Probability of 'true'	Float	The probability that the outcome of each trial is true.
Number of trials	Int	The number of independent experiments performed, each one with probability of success Probability of 'true'.

• Cauchy

Probability distribution that resembles a normal distribution but with a taller peak, whose tails decay slower. Its parameters are the location of the peak and the scale - the latter defines its width.

Attribute	Type	Description
Location	Float	Defines where the peak is.
Scale	Float	Half the width of the probability density function at half the maximum height.

• Chi_Squared

Probability distribution of a sum of the squares of a number of independent normal random variables. The number of normal random variables is called the degrees of freedom of the Chi-squared distribution.

Attribute	Type	Description
Degrees of freedom	Float	Number of independent normal random variables that are summed.

• Exponential

Probability distribution of the time between events in a Poisson process. Its parameter is the rate at which the events in the Poisson process occur.

Attribute	Type	Description
Rate	Float	Rate at which the events in the Poisson process occur.

• Extreme_Value

Limit distribution of properly normalized maxima of a sequence of independent and identically distributed random variables.

Attribute	Type	Description
Location	Float	Defines where the peak is.
Scale	Float	Defines how spread out the values are.

• Fisher_F

Ratio of two independent random variables with chi-squared distributions, each one divided by its corresponding number of degrees of freedom for scaling.

Attribute	Type	Description
Denominator Dof	Float	Degrees of freedom of the chi-squared random variable in the denominator.
Numerator DoF	Float	Degrees of freedom of the chi-squared random variable in the numerator.

• Gamma

Maximum entropy probability distribution for a random variable, whose mean is the product between the shape and scale, which are the two parameters of the Gamma distribution.

Attribute	Type	Description
Shape	Float	Modifies the shape of the probability distribution.
Scale	Float	Defines how spread out are the values.

• Geometric

The probability distribution of the number of experiments with a Bernoulli distribution needed to get one success.

Attribute	Type	Description
Data Type	Drop-down	Whether the output is an Int or Byte.
Probability of 'true'	Float (between 0 and 1)	The probability of success in the Bernoulli trials.

• Lognormal

Probability distribution of a random variable whose logarithm has a normal distribution.

Attribute	Type	Description
Mean	Float	The mean value of the logarithm of the distribution.
Standard deviation	Float	The standard deviation of the logarithm of the distribution.

• Negative_Binomial

Probability distribution of the number of successes in a sequence of independent experiments, each with two possible outcomes: success and failure, before a specified non-random number of failures occur. The parameters for this probability distribution are the probability of a successful outcome in each experiment and the number of failures until the experiments stop.

Attribute	Type	Description
Data Type	Drop-down	Whether the outcome is an Int or Byte.
Probability of 'true'	Float (between 0 and 1)	The probability that the outcome of each trial is true.
Number of trials	Int	The number of failures to occur until the experiments stop.

• Normal

Symmetric probability distribution, with half its values less than the mean and half greater than the mean. The parameters are the mean, which equals the median and the mode, and the standard deviation.

Attribute	Type	Description
Mean	Float	The mean value of the distribution.
Standard deviation	Float	The standard deviation of the distribution.

• Poisson

Discrete probability distribution that expresses the probability of a given number of events occurring in a specified time period. Its parameter is the mean value.

Attribute	Type	Description
Data Type	Drop-down	Wheter the outcome will be an Int or Byte.
Mean	Float	The mean value of the distribution.

• Student_T

Probability distribution that arises when estimating the mean of a normally-distributed statistical population with a small sample size and unknown standard deviation. Its parameter is the number of degrees of freedom, which is the number of observations taken from a normal distribution minus one.

Attribute	Type	Description
Degrees of freedom	Float	The number of observations taken from a normal distribution minus one. As it grows, the Student-t distribution approaches a normal distribution.

• Uniform

Probability distribution in which all the values in an interval are equally likely to be drawn. It can either be continuous or discrete.

Attribute	Type	Description
Data Type	Drop-down	Whether an Int, Float, or Byte will be generated.
Minimum	Defined in the Data Type Attribute	The lower bound of the interval from which the random number will be extracted.
Maximum	Defined in the Data Type Attribute	The upper bound of the interval from which the random number will be extracted.

• Weibull

Attribute	Type	Description
Shape	Float	Defines the shape of the probability distribution.
Scale	Float	Defines how spread out the values of the probability distribution are.

Standard

This Mode only uses a uniform distribution. It can either be discrete or continuous.

Distribution

Attribute	Type	Description
Data Type	Drop-down	Whether an Int, Float, or Byte will be generated.
Minimum	Defined in the Data Type Attribute	The lower bound of the interval from which the random number will be extracted.
Maximum	Defined in the Data Type Attribute	The upper bound of the interval from which the random number will be extracted.

Inputs

Input	Type	Description
Pulse Input (►)	Pulse	A standard Input Pulse, to trigger the execution of the Node.

Outputs

Output	Type	Description
Pulse Output (►)	Pulse	A standard Output Pulse, to move onto the next Node along the Logic Branch, once this Node has finished its execution.
Output	Depends on the Mode and Distribution	The random outcome that was generated.

External Links

• Random number generation on Wikipedia.

• Pseudorandom number generator on Wikipedia.

• List of probability distributions on Wikipedia.